A New Method of Reducing Pair-wise Combinatorial Test Suite
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Bibliographic record
Abstract
The biggest problem for combinatorial test is a numerous number of combinations of input parameters by combinatorial explosion. Pair-wise combinatorial coverage testing is an effective method which can reduce the test cases in a suite and is able to detect about 70% program errors. But, under many circumstances, the parameters in programs under test (PUTs) have relations with each other. So there are some ineffective test cases in pair-wise combinatorial test suites. In this paper, we propose a method of reducing ineffective combinatorial test cases from pair-wise test suite. The main ideas of the method is that we firstly analyzes the dependent relationships among input parameters, then use the relationships to reduce ineffective pair-wise combinations of input parameters, and lastly generate the pair-wise combinatorial coverage test suite. The experiments show that the method is feasible and effective, and considerably reduce the number of pair-wise combinatorial test cases for some programs under test.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it